MétaCan
Menu
← Back to cohort
Record W4392207429 · doi:10.5751/es-14828-290124

Knowledge, perception, and awareness of society regarding (over)abundance of wild ungulate populations

2024· article· en· W4392207429 on OpenAlexvenueno aff
Antonio J. Carpio, Pelayo Acevedo, Rafael Villafuerte‐Jordán, Rocío Serrano Rodriǵuez, Roberto Pascual‐Rico, María Martínez‐Jauregui

Bibliographic record

VenueEcology and Society · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
FundersEuropean Social FundAgencia Estatal de InvestigaciónUniversidad de Castilla-La ManchaEuropean Commission
KeywordsUngulateAbundance (ecology)PerceptionGeographyEcologyBiologyHabitat

Abstract

fetched live from OpenAlex

Social perception of ungulates and their management depends on both their abundance and the socioeconomic context. However, an approach that addresses this issue is currently unavailable. Our objective was to employ a survey in Spain (n = 440) to evaluate the knowledge and perceptions on the eight species of wild ungulates that inhabit the Iberian Peninsula, and their abundance. The results showed that respondents were unaware of the existence of many of the species. Only wild boar (95% of the surveyed population) and red deer (72%) were widely identified. Respondents perceived that urban, agricultural, and livestock contexts were the most frequent suffering overabundance, with the wild boar as most relevant species (86%). This study illustrates how a better understanding of overabundance and public perceptions is important to ensure effective communication on ungulate population status and to improve public support for their management, thus avoiding bias toward certain species, impacts, or specific contexts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.271
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueEcology and Society→Same topicWildlife Ecology and Conservation→French-language works237,207→